Human Reference Atlas Literature Knowledge Graph
Bibliographic record
Abstract
The HRA Literature Knowledge Graph (HRAlit KG, v0.7.1) represents 8.78 million scholarly articles from the PubMed database that are associated with human anatomical structures and cell types that are incorporated into the Human Reference Atlas (HRA) project and its data products. We use Schema.org vocabulary for entities (e.g., schema:ScholarlyArticle, schema:Person, & schema:Periodical) and predicate relationships (e.g., schema:author, schema:about, schema:partOf) to model publications. The HRAlit KG can be mined to identify leading experts, major papers, or alignment with existing ontologies in support of systematic HRA construction and usage. In addition to scholarly articles data, the HRA Literature knowledge graph incorporates: Human Reference Atlas (HRA KG, v2.3), collections group multiple HRA Digital Objects (DOs) for use in specific applications, and includes all public dataset graphs in the HRA Knowledge Graph (Bueckle et al. 2025). It includes HRA DO types with a DOI. As of HRA v2.3, those are 2d-ftu, asct-b, ctann, omap, ref-organ, and vascular-geometry DO types. Other DO types may be added in the future as they receive DOIs (https://purl.humanatlas.io/graph/hra-lit). Human Reference Atlas API Collection, a part of the HRA KG, the hra-api centers on asct-b and associated 3D models (ref-organ, landmark) used in various HRA applications for visualizing and organizing reference data. (https://purl.humanatlas.io/collection/hra-api) Common Coordinate Framework Ontology (CCF) provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions of tissue blocks and organs linked to existing ontologies. (https://purl.humanatlas.io/graph/ccf). Simple Standard for Sharing Ontology Mappings (SSSOM) between Medical Subject Headings and UBERON and Cell Ontologies that provide exact conceptual matches (skos:exactMatch) between MeSH descriptors and anatomical structures and cell types found in the HRA KG (https://purl.humanatlas.io/graph/mesh-uberon-cl-human-mapping) Simple Standard for Sharing Ontology Mappings (SSSOM) between Pubmed Articles and Subject Headings and UBERON and Cell Ontologies that provide related conceptual matches (skos:relatedMatch) between PubMed articles and anatomical structures and cell types found in the HRA KG (https://purl.humanatlas.io/graph/pubmed-uberon-cl-mapping). Medical Subject Headings (MeSH) RDF linked data representation of the MeSH biomedical vocabulary, produced by the National Library of Medicine(http://id.nlm.nih.gov/mesh/). Uber-anatomy Ontology (v20250528), an integrated cross-species anatomy ontology representing a variety of entities classified according to traditional anatomical criteria such as structure, function and developmental lineage. (Mungall et al. 2012). The ontology includes comprehensive relationships to taxon-specific anatomical ontologies, allowing integration of functional, phenotype and expression data (https://purl.humanatlas.io/vocab/uberon). Limitations: Person and organization names are reproduced as reported by National Library of Medicine and have not been pre-processed to disambiguate identities prior to inclusion into the HRAlit KG. Application of disambiguation methods are strongly recommended prior to analysis of authorship trends and relationships. SSSOM mappings between pubmed articles and UBERON and Cell Ontology concepts were generated using embedding models and have not been validated by subject matter experts. Use these triple statements in analysis with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.064 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".